Noah Hollmann
Impact in
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- Artificial Intelligence in Healthcare and Education
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- Artificial Intelligence in Healthcare
Papers in
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- Imbalanced Data Classification Techniques 1
- Semantic Web and Ontologies 1
- Machine Learning and Data Classification 1
- Explainable Artificial Intelligence (XAI) 1
- Topic Modeling 1
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- Bioinformatics and Genomic Networks 1
- Co-authors
- Frank Hutter (2 shared papers)Samuel Müller (3 shared papers)Robin Tibor Schirrmeister (1 shared paper)Lennart Purucker (1 shared paper)Carsten Eickhoff (1 shared paper)Jakob Steinfeldt (1 shared paper)John Deanfield (1 shared paper)Thore Buergel (1 shared paper)
- Journals
- Nature (1 paper)The Lancet Digital Health (1 paper)FreiDok plus (Universitätsbibliothek Freiburg) (1 paper)CLEF (Working Notes) (1 paper)
- Partner nations
- United KingdomGermany
In The Last Decade
Noah Hollmann
5 papers receiving 139 citations
Noah Hollmann's Hit Papers
Peers
Comparison fields: 5 of 88
- Health Informatics 7
- Health Information Management 8
- Fuel Technology 1
- Artificial Intelligence 25
- Radiology, Nuclear Medicine and Imaging 14
Countries citing papers authored by Noah Hollmann
This map shows the geographic impact of Noah Hollmann's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Noah Hollmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Noah Hollmann more than expected).
Fields of papers citing papers by Noah Hollmann
This network shows the impact of papers produced by Noah Hollmann. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Noah Hollmann. The network helps show where Noah Hollmann may publish in the future.
Co-authors
The 14 scholars most cited alongside Noah Hollmann, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Accurate predictions on small data with a tabular foundation model Hit paper breakdown → | 2025 | 106 |
| 2 | 2022 | 29 | |
| 3 | Ranking and Feedback-based Stopping for Recall-Centric Document Retrieval. | 2017 | 4 |
| 4 | 2024 | 2 | |
| 5 | 2023 | 1 |
About Noah Hollmann
Noah Hollmann is a scholar working on Artificial Intelligence, Molecular Biology, Cardiology and Cardiovascular Medicine, Information Systems and Genetics, having authored 5 papers that have together received 142 indexed citations. Recurring topics across this work include Imbalanced Data Classification Techniques (1 paper), Semantic Web and Ontologies (1 paper), Machine Learning and Data Classification (1 paper), Genetic Associations and Epidemiology (1 paper), Bioinformatics and Genomic Networks (1 paper), Explainable Artificial Intelligence (XAI) (1 paper), Topic Modeling (1 paper) and Cardiovascular Function and Risk Factors (1 paper). The work is most often cited by research in Health Informatics (7 citations), Health Information Management (8 citations), Fuel Technology (1 citation), Artificial Intelligence (25 citations) and Radiology, Nuclear Medicine and Imaging (14 citations). Noah Hollmann has collaborated with scholars based in United Kingdom and Germany. Frequent co-authors include Frank Hutter, Samuel Müller, Robin Tibor Schirrmeister, Lennart Purucker, Carsten Eickhoff, Jakob Steinfeldt, John Deanfield, Thore Buergel, Brian A. Ference and Julius Upmeier zu Belzen. Their work appears in journals such as Nature, The Lancet Digital Health, FreiDok plus (Universitätsbibliothek Freiburg) and CLEF (Working Notes).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.